Runway Visual Range Research Articles

Nighttime measurement errors in the runway visual range and optimization of runway lighting systems

Considering that existing theories consider runway lights as markers, neglecting their roles as lighting devices that increase sky background luminance and thus affect runway visual range (RVR) measurements, the actual values of sky background luminance observed by pilots during take-off are calculated and compared with the instrumental RVR measurements in this paper. The RVR measurement errors due to the installation positions of these devices are subsequently analysed. Furthermore, the impacts of different runway light types and positions on sky background luminance are explored, leading to the proposal of an optimized runway lighting scheme. The results show that the representation of sky background luminance sensor data in RVR measurement systems for nighttime airport runways is limited. A deviation of over 90% exists between the sky background luminance of the runway entrance and the RVR measurement equipment location, resulting in a relative error in RVR measurements of over 18%. The centreline and entrance lights contribute significantly to the total sky background luminance. Lights closer to the observer contribute more luminance. In the second proposed scheme, modifying the lighting range to 600 m yields the highest RVR improvement, with an optimization magnitude of over 5% across all visibility ranges.

Efficient prediction of runway visual range by using a hybrid CNN-LSTM network architecture for aviation services

Efficient prediction of runway visual range by using a hybrid CNN-LSTM network architecture for aviation services

A new transmissometer method that measures runway visibility across a short distance

<p><span lang="EN-US">The present technique developed a transmissometer idea for intensity measurements in the ecosystem across an 80 cm distance. The method relies on the transmission of an optical or infrared (diode laser 785 nm) signal obtained on spectrum analyses (ocean optics HG4000) received signal from the laser under unique conditions (light dust, heavy dust, and waterdrops) at a short distance. Thus, this approach is useful to measure the intensities of the laser beam and locate the visibility measurements of laser transmissometers in the rain and special care to the dust. The results showed that heavy dust enhanced the visibility measurements for distinct intensity distributions between showers and dust. Since light and heavy dust within a single transmittance can easily cover the entire runway visual range (RVR) range for a brief period, visibility measurements were crucial. The full-scale value corresponds to infinite visibility for heavy dust (0.8611) and minimum visibility for drop rain conditions (0.01073).</span></p>

Comparative Analysis of Instrumental and Manual Visibility Observations at Xiamen Airport and the Influence of Relative Humidity

Visibility is a major factor affecting the safety and efficiency of aviation operations. As instrumental visibility measurements are the basis for calculating runway visual range, measurement accuracy is crucial. In this study, we collected instrumental visibility data (VI) from two sets of Vaisala transmissometers (referred to as LT05 and LT23) and one set of forward-scatter meters (referred to as FD) installed at Xiamen Airport. We also considered manually observed visibility (VM) and relative humidity (RH) records of automatic weather stations for 2015 to 2020. Taking the VM data as the benchmark, we comprehensively evaluated the difference between the VI of each instrument and VM as well as the influence of RH on such deviations based on the deviation (ΔV), relative deviation (ΔVMdi), root mean square error (RMSE), relative-RMSE, mean value, and correlation analysis method. Our results showed that: (1) among the three sets of visibility meters, the VI values of LT05 were the closest to the VM values under different VM levels and at high RH. Deviations in the FD measurements were greater than those of the two LT sets under low-visibility conditions that significantly affect aviation operations (VM < 800 m). (2) In general, the VI values were lower than the VM values, and the larger the VM value, the greater the deviation. (3) Extremely large ΔV and ΔVMdi values appeared in spring when the visibility level was rapidly increasing or decreasing. (4) The FD results were greatly affected by RH, with higher proportions of large ΔV and large ΔVMdi data than the two LT sets under high-humidity conditions. Therefore, the Vaisala transmissometers outperformed the forward-scatter meter at the airports along the southeastern coast of China and those in high-humidity environments.

Visibility classification and influencing-factors analysis of airport: A deep learning approach

This paper discusses the visibility classification and influencing-factor analysis of an airport, which were carried out through a data-driven deep learning approach and multiple nonlinear regression analysis. First, a deep integrated model for airport visibility classification was developed, and the relationship between runway visual range (RVR) and surface meteorological elements for different reasons was analyzed. To extract more low-level features from nighttime images, the original images were transformed into pseudo-color images, and the image pairs were input into the classification model. The proposed deep integrated model combined two popular convolutional neural network (CNN) models: VGG-16 and Xception. The results showed that the accuracy of the proposed model was 87.64%, with the F1-score being 88.58%; it achieved the best performance among other well-known experimental CNNs. Afterward, the relationships between RVR and meteorological optical range (MOR), source light intensity (I), and background light (BL) were analyzed, and the results showed that RVR increased with increasing I under the same MOR and BL conditions; however, it decreased with increasing log (BL) values under the same I and MOR conditions. RVR increased as MOR increased when other conditions remained unchanged. Subsequently, the multivariate regression analysis was conducted to fit the relationship between MOR and surface meteorological elements, and the results showed that the cubic curve was the best-fit curve with R-square values of 0.937 and 0.860 in December 2019 and March 2020, respectively. Finally, combined with the analysis of the factors influencing the RVR, a significant negative correlation was observed with relative humidity, along with a significant positive correlation with temperature and a positive correlation with average wind speed in the winter of 2019 and spring of 2020. However, the pressure factor showed the opposite correlation, possibly because the arrival of cold air in winter increases the ground pressure, which is conducive for the dissipation of pollutants. Meanwhile, the increase in temperature and decrease in pressure in spring increases the chance of disturbances in the atmosphere, thereby increasing the likelihood of adverse visibility factors. ◆ Pseudo-color images were found beneficial for improving the accuracy of visibility identification. ◆ An integrated model combined VGG-16 and Xception achieved the best performance compared to that of other popular models. ◆ The relationship between RVR and MOR, I, and BL was found. ◆ The multivariate regression analysis between MOR and surface meteorological elements in different seasons was conducted. ◆ There is a correlational relationship between RVR and surface meteorological elements.

Optical Runway, Stealth Runway, Carbon Capture and High-Quality Air in a Novel EEMs Green & Clean Energy Airfield

<div class="section abstract"><div class="htmlview paragraph">A novel aviation infrastructure system by a multilayer ecological & energized module (EEM) system is aiming to create a high safety & security, graceful and comfortable air travel environment, high-quality air, zero energy, zero-water-consumption, and zero-carbon with a 100% greening rate, etc. It contains a modular EEM optical runway, totally-enclosed EEM ecological modules, and EEM ecological modules with thin-film or silicon solar cells on the top layer, etc. With the light processed and enhancing and optimizing crushed shadows & blown highlights and high contrast color layout, the EEM optical runway has a powerful visual flight, runway visual range (RVR), and significant blown highlights in 3D position and posture in various complex weather and occasions for the instrument landing, manually land and taxiing, etc. It’s always clean and bright without lampblack and exhaust traces since it is easy to rinse and replace. The totally-enclosed EEM system is laid flat on the entire airfield, including the ceiling and flooring of aircraft as well. It will generate enough clean energy, provide enough high-quality air with plentiful oxygen, plant anions, floral, phytoncide, active constituents, and fragrant scents with low-concentration CO<sub>2</sub> for passengers and staff, and completely isolate birds’ food chains and habitat, etc. With controllable photosynthetic acceleration, the EEM system is a powerful carbon capture system with significant positive spillover. With antiskid and rainwater steerable structures in the EEM, the planes have calculable security assurances in severe weather and catastrophic events. Hot, cold water and steam system without electromagnetic interference in EEM are used for hot waves and snow and ice.</div></div>

Forecast model of airport haze visibility and meteorological factors based on SVR-RBF model

All kinds of meteorological elements near the airport directly affect the dissipation process of fog, which also affects the visibility. The correlation analysis of temperature, humidity, dew point temperature, air pressure, wind speed and other meteorological factors with visibility Runway Visual Range (RVR)and Meteorological Optical Visual Range (MOR)was carried out by SPSS. It was found that the four variables, TEMP, RH, WS2A and PAINS, had great influence. In this paper, the above four variables are selected as inputs to nonlinear fit the RVR value of visibility. Combined with Radial Basis Function (RBF), it is found that SVM-RBF model has better generalization ability and fitting degree.

The Use of Enhanced Flight Vision Systems (EFVS) for Low-visibility Takeoffs in Part 121 Operations

Twelve Part 121 operations commercial-carrier crews completed low-visibility takeoffs at Memphis International Airport using an Enhanced Flight Vision System (EFVS). A 2x2x2x3 factorial design with runway visual range (RVR; 500 and 700 feet), runway edge lighting (high intensity or medium intensity) and two levels of EFVS (either captain’s head-up display only or with additional first officer’s head-down repeater) was used along with supplemental sample points and several baseline trials representing current-authorization conditions. Tasks included normal takeoffs, EFVS failure (both continue and reject trials), and engine failure (reject). There were no significant main effects of display or infrastructure in the main design (500, 700 RVR), and pilot performances in the experimental trials with EFVS were not markedly different from the baseline (current authorization) trials. All crews were able to stop the aircraft successfully on the runway during rejected takeoffs. Pilots uniformly believed they could successfully complete takeoffs or reject them in lower visibilities with EFVS as compared with using the head-up display without EFVS, which was sup-ported by observed performance.

Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain

This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem.

Examining the Compellingness of electronic Low Visibility Taxi Charts

The purpose of this study was to examine whether the presentation of own-aircraft (ownship) position was compelling when presented on electronic low visibility taxi charts. Although airport charts showing ownship position have been in use for some time, ownship position was not available on low visibility taxi charts because these charts were not geo-referenced. Twenty Airline Transport Pilots (ATP) (10 flightcrews) participated in a simulator study in which they performed six taxi scenarios in three different levels of visibility (1200 Runway Visual Range (RVR), 600 RVR, 300 RVR) using an electronic chart application on an iPad. Ownship position was shown on the chart for half the scenarios. In one scenario, we simulated a position error. We collected objective data (taxi speed, taxi time, fixation and dwell time), and pilot opinions on the usability of the electronic chart application. The results showed that no incursions/excursions were committed. All flightcrews noticed the error in ownship position, when it occurred; in fact, they also noticed other errors in ownship position that were not planned as part of the experiment design. Captains looked more often at the electronic chart when ownship position was presented than when it was not, regardless of visibility conditions. Additionally, Captains’ percentage of fixations were almost equal between the electronic chart and out-the-window. Such behavior may reflect the perceived utility of showing ownship on the electronic chart and may be an indication of the compelling nature of that information source.

Pilot Performance Using Head-up and Head-down Synthetic-Vision Displays for SA CAT I Approaches

Thirteen two-person crews flew 39 approaches each to runway 34R at Seattle-Tacoma airport in a 737-800 level D flight simulator using both head-up and head-down synthetic-vision displays. The within-subject design manipulated display type, runway-lighting configuration, ambient illumination, and two combinations of decision height and runway visual range associated with Special Authorization Category I and II approaches. Data collected/analyzed included training trials, instrument landing system tracking, touchdown point and vertical velocity at touchdown, and pilot opinions on the adequacy of the display formats for the specific task. Results indicated that there was a small but reliable difference in touchdown point between day and night approaches (longer in day) and that distance from centerline was also slightly affected by display type. Mean vertical velocity at touchdown was also affected slightly. Data indicated that there appeared to be no operationally significant differences as a function of display type or other variables examined.

Prediction of low-visibility events due to fog using ordinal classification

The prediction of low-visibility events is very important in many human activities, and crucial in transportation facilities such as airports, where they can cause severe impact in flight scheduling and safety. The design of accurate predictors for low-visibility events can be approached by modelling future visibility conditions based on past values of different input variables, recorded at the airport. The use of autoregressive time series forecasters involves adjusting the order of the model (number of past series values or size of the sliding window), which usually depends on the dynamical nature of the time series. Moreover, the same window size is normally used for all the data, thought it would be reasonable to use different sliding windows. In this paper, we propose a hybrid prediction model for daily low-visibility events, which combines fixed-size and dynamic windows, and adapts its size according to the dynamics of the time series. Moreover, visibility is labelled using three ordered categories (FOG, MIST and CLEAR), and the prediction is then carried out by means of ordinal classifiers, in order to take advantage of the ordinal nature of low-visibility events. We evaluate the model using a dataset from Valladolid airport (Spain), where radiation fog is very common in autumn and winter months. The considered data set includes five different meteorological input variables (wind speed and direction, temperature, relative humidity and QNH – pressure adjusted at mean sea level) and the Runway Visual Range (RVR), which is used to characterize the low-visibility events at the airport. The results show that the proposed hybrid window model with ordinal classification leads to very robust performance prediction in daily time-horizon, improving the results obtained by the persistence model and alternative prediction schemes tested.

Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is $${>}$$ 1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ( $${<}$$ 500 m). However, we show improved results of all the methods when data from a neighbouring meteorological tower are included, and also with a pre-processing scheme using a wavelet transform. Also presented are results of the algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

An Exploratory Evaluation of General Aviation Pilot Performance and Preferences Using Synthetic Vision Displays for Approaches and Missed Approaches in Flat and Challenging Terrain

Eight instrument-rated General Aviation pilots completed CAT I SA approaches to Oklahoma City (KOKC) and localizer approaches to Aspen (KASE), the latter with missed approaches. Approaches to KOKC were conducted in two levels of runway visual range (RVR) with two decision heights (DH) using a head-up or head-down display with Synthetic Vision (SV) imagery. The lower DH led to significantly more completed approaches while the higher DH regularly caused pilots to execute missed approaches. RVR was not a significant factor for those values examined. Although SV was expected to be more beneficial for missed approaches in mountainous terrain (KASE) than for initial approaches, terrain clearance achieved was not influenced by display format. Cross-track and glide-slope root-mean-square error for the approaches to KOKC did not differ reliably between display conditions. Pilots uniformly believed they could successfully and comfortably complete approaches in lower visibilities if SV equipped as compared with other displays.

LVP conditions at Mohamed V airport, Morocco: Local characteristics and prediction using neural networks

Low visibility and/or ceiling conditions have a strong impact on airports' traffic and their prediction is still a challenge for meteorologists. In this paper, the local characteristics of Low Visibility Procedure (LVP) conditions are investigated and the artificial neural network (ANN) based on resilient backpropagation as supervised learning algorithm is used to predict such meteorological conditions at Mohamed V international airport, Casablanca, Morocco. This article aims to assess the ANN ability to provide accurate prediction of such events using the meteorological parameters from the Automated Weather Observation Station (AWOS) over the period from January 2009 to March 2015. First, LVP conditions were classified according to their classes (fog LVP and no fog LVP) and their sources (Runway Visual Range -RVR LVP-, Ceiling -HCB LVP- or both) for both runway end points (35R and 17L). It is found that most of LVP conditions are associated with fog and are often due to decreasing of RVR below 600m. Next, Eleven ANNs were developed to produce LVP prediction for consecutive hourly valid forecast times covering the night and early morning. The Multi-Layer Perceptron (MLP) architecture with one hidden layer is used in this study. Results show that ANNs are able to well predict the LVP conditions and are robust to errors in input parameters for a relative error below 10%. Furthermore, it is found that the ANN's skill is less sensitive to LVP type being predicted.

The Development of GPS Navigation Systems in Civil Aircraft

The industry of civil aviation is developing in a fast manner to occupy the increasing needs for a fast, safe and comfortable transportation. To achieve these needs, some effective programs, plans, and systems designs are required. The current reliable aircraft navigation and landing systems have relatively a low level of accuracy especially in aircraft landing stage. The best landing category could be achieved requires a runway visual range up to 50 meters and automatic landing system is not in operation yet all over the world. Aircraft navigates using Radar, Radio and Satellite navigation systems. The Federal Aviation Administration (FAA) plans to replace legacy navigation systems with satellite based navigation technology. Currently, the Global Positioning System (GPS) and its augmentations is the main satellite navigation system used all over the world for air navigation applications. The development of aircraft navigation has been performed with the enhancement of GPS augmentation systems. In this paper, aircraft navigation systems and techniques will be presented in this paper to evaluate the integrity and reliability of each system, and to make a comparison among these systems according to accuracy, integrity and availability.

Parameterization of Runway Visual Range as a Function of Visibility: Implications for Numerical Weather Prediction Models

Abstract A parameterization of runway visual range (RVR) has been developed using relevant meteorological parameters such as visibility (Vk), relative humidity (RH), temperature (T), precipitation intensity (PI), and precipitation type (PT) measured in years between 2009 and 2011 at Toronto Pearson International Airport during the Canadian Airport Nowcasting Project. The FD12P probe measured PI, Vk, and PT. The observed Vk and PT were tested against data reported by hourly surface observations (SAs). The measured Vk has correlated well with the SA with a correlation coefficient (r) of 0.76 for Vk &amp;lt; 5 km, but the FD12P underestimated visibility by about 20% with a mean difference (MD) of about 196 m. For Vk &amp;lt; 2 km, the FD12P overestimated visibility by about 7% with an MD of 60 m. The SA reported slightly more snow events—22% as compared to 17%—but the FD12P reported many more snow grain cases than the SA. Both the SA and the FD12P reported rain at similar frequency—4% and 5%, respectively. Using a theoretical approach, a parameterization that can be used to determine RVR as a function of Vk has been developed. Using the observed T, RH, and dewpoint temperature (Td), a new parameterization for predicting Vk/RVR in fog has been also developed. These parameterizations agreed with observations (r ≈ 0.8). The parameterizations have been tested using the Canadian Environmental Multiscale Regional model. The results show that when PI, RH, and T are reasonably predicted and the fog events are correctly diagnosed, the model can be used to forecast RVR.

Directional variation of visual range due to anisotropic atmospheric brightness

The meteorological optical range (MOR) is the greatest distance at which a nonluminous object is barely discernible. Koschmieder related the optical atmospheric extinction coefficient to the contrast between the object and its background. His law assumes a uniform atmosphere and yields an isotropic value of the MOR. The model presented here incorporates the effect of anisotropic atmospheric brightness on contrast (and thus on the MOR). The Koschmieder MOR is then decreased by a factor according to the brightness prevailing in the direction of assessment. Manual assessments of the MOR made in arbitrary directions are shown to agree satisfactorily with the derated MOR. Minor modifications to existing instruments at airports would enable the instruments to register true values of the MOR and the runway visual range in directions relevant to the pilot, i.e., along the runway.

Advanced Taxi Map Display Design for Low-Visibility Operations

Conducting gate to gate operations during reduced visibility conditions is a major impediment to scheduled and unscheduled flight operations in the National Airspace System (NAS). Takeoff and landing minima are predicated on aircraft equipage and airport visibility (e.g., at some major airports, operations are terminated when visibility is below 700 ft runway visual range (RVR). Although some aircraft can land with zero-zero visibility, there are no ground or flight deck systems that allow them to taxi under low visibility conditions. A map display system designed to support low visibility taxi was evaluated by 12 B-747 flight crews in NASA's Crew Vehicle System Research Facility (CVSRF). Three taxi-map conditions were compared: paper map only, basic moving map, and advanced moving map. Crews landed and taxied along 24 different taxi routes under three visibility conditions: unlimited visibility, 700 ft RVR, and 300 ft RVR. Taxi time, errors and workload were collected for each taxi operation. Video tape recordings captured crew interactions and head-up and head-down times. Taxi times and errors were significantly better for crews with electronic maps than for crews with a paper map. Although crews with the advanced map experienced significantly more head-down time, the head down interval was significantly less than with the paper map and crew workload was significantly less. During the post-flight design review, pilots identified improvements in procedures and formatting that might enhance performance. They developed a procedure for safely switching from the NAV display to the map; and in general their comments were very favorable.

Flight test certification of multipurpose head-up display for general aviation aircraft

Advances in flight display technology have enabled manufacturers to provide a head-up display (HUD) of aircraft control and performance parameters for use in general aviation aircraft without the use of inertial platforms or specialized data bus architecture. The Federal Aviation Administration currently has no regulatory guidance regarding HUD or flight symbology certification. A specific certification pro- gram is presented. The process of categorizing the HUD as a supplemental system instead of a primary display is discussed. Flight display symbology is discussed, and the final symbology set is presented. The certification flight test plan is discussed including the requirements for functional, usability, and opera- tional testing. Flight test data are discussed. A HUD system was certified for single-pilot use in instrument meteorological conditions in a general aviation aircraft. HE head-up-displ ay (HUD) was first developed in mili- tary aircraft from reflecting optical sights. The HUD places flight and navigation data in the pilot's forward field of view (FOV). These data are presented through various symbols as a collimated image that appears to be floating at infinity. Early HUDs were used for weapons delivery with displays tailored for a single task. HUDs today can provide a variety of aircraft state and navigation data. Recent HUD develop- ment, as an enhancement tool to facilitate manually flown in- strument approaches to weather minima as low as a runway visual range of 1200 ft, has obvious implications for air carrier operations. The ability to ease the instrument-to-visual ap- proach transition and acquire the runway environment sooner is a desirable feature of any cockpit display system. Though these systems usually require high-fidelity inertial reference platforms and are costly, the advantage gained in arrival reli- ability has been deemed cost effective. Additionally, because the pilot has state and navigation data available while looking through the aircraft windscreen, the increased ability to visu- ally clear for other traffic is a safety enhancement. Current HUD development and operational concept are toward dis- plays that are useful and certifiable during all phases of flight. Lower cost displays that do not provide enhanced capability, but do provide increased out-of-the-cockpit awareness for cor- porate and general aviation aircraft are also emerging into the marketplace. A typical HUD displays airspeed, altitude, pitch and roll attitude, and heading. In addition, navigation or mission data can usually be selected for display. Flight director modes, radar altitude, and marker beacon passage can also be shown, if available. Most HUDs can also display master warning and caution annunciators. The subject HUD was targeted for the corporate and general aviation industry. This HUD differed from other operational systems in that it used conventional aircraft gyros and did not depend on an inertial navigation system platform for attitude information. Since conventional aircraft gyros are less precise than inertial platforms, the HUD symbology was not confor-